{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import and setting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ---------- import\n",
    "import pickle\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ---------- figure size\n",
    "plt.rcParams['figure.figsize'] =[8, 6]\n",
    "\n",
    "# ---------- axes\n",
    "plt.rcParams['axes.grid'] = True\n",
    "plt.rcParams['axes.linewidth'] = 1.5\n",
    "\n",
    "# ---------- ticks\n",
    "plt.rcParams['xtick.direction'] = 'in'\n",
    "plt.rcParams['ytick.direction'] = 'in'\n",
    "plt.rcParams['xtick.major.width'] = 1.0\n",
    "plt.rcParams['ytick.major.width'] = 1.0\n",
    "plt.rcParams['xtick.major.size'] = 8.0\n",
    "plt.rcParams['ytick.major.size'] = 8.0\n",
    "\n",
    "# ---------- lines\n",
    "plt.rcParams['lines.linewidth'] = 2.5\n",
    "\n",
    "# ---------- grid\n",
    "plt.rcParams['grid.linestyle'] = ':'\n",
    "\n",
    "# ---------- font\n",
    "plt.rcParams['font.size'] = 20\n",
    "#plt.rcParams['pdf.fonttype'] = 42    # embed fonts in PDF using type42 (True type)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_pkl(filename):\n",
    "    with open(filename, 'rb') as f:\n",
    "        return pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Spg_num</th>\n",
       "      <th>Spg_sym</th>\n",
       "      <th>Spg_num_opt</th>\n",
       "      <th>Spg_sym_opt</th>\n",
       "      <th>E_eV_atom</th>\n",
       "      <th>Magmom</th>\n",
       "      <th>Opt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>184</td>\n",
       "      <td>P6cc</td>\n",
       "      <td>184</td>\n",
       "      <td>P6cc</td>\n",
       "      <td>-635.358268</td>\n",
       "      <td>NaN</td>\n",
       "      <td>done</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>164</td>\n",
       "      <td>P-3m1</td>\n",
       "      <td>164</td>\n",
       "      <td>P-3m1</td>\n",
       "      <td>-635.286573</td>\n",
       "      <td>NaN</td>\n",
       "      <td>done</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>82</td>\n",
       "      <td>I-4</td>\n",
       "      <td>82</td>\n",
       "      <td>I-4</td>\n",
       "      <td>-635.267265</td>\n",
       "      <td>NaN</td>\n",
       "      <td>done</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>108</td>\n",
       "      <td>I4cm</td>\n",
       "      <td>140</td>\n",
       "      <td>I4/mcm</td>\n",
       "      <td>-635.143664</td>\n",
       "      <td>NaN</td>\n",
       "      <td>done</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>102</td>\n",
       "      <td>P4_2nm</td>\n",
       "      <td>35</td>\n",
       "      <td>Cmm2</td>\n",
       "      <td>-635.091983</td>\n",
       "      <td>NaN</td>\n",
       "      <td>done</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Spg_num Spg_sym  Spg_num_opt Spg_sym_opt   E_eV_atom  Magmom   Opt\n",
       "0      184    P6cc          184        P6cc -635.358268     NaN  done\n",
       "2      164   P-3m1          164       P-3m1 -635.286573     NaN  done\n",
       "1       82     I-4           82         I-4 -635.267265     NaN  done\n",
       "4      108    I4cm          140      I4/mcm -635.143664     NaN  done\n",
       "3      102  P4_2nm           35        Cmm2 -635.091983     NaN  done"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rslt_data = load_pkl('./pkl_data/rslt_data.pkl')\n",
    "\n",
    "# ---------- sort by Energy\n",
    "rslt_data.sort_values(by=['E_eV_atom']).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data: 5\n",
      "Success: 5\n",
      "Error: 0\n",
      "Emin: -635.3582675543112 eV/atom\n"
     ]
    }
   ],
   "source": [
    "# ---------- Number of structures\n",
    "ndata = len(rslt_data)\n",
    "print('Number of data: {}'.format(ndata))\n",
    "\n",
    "# ---------- check success and error\n",
    "nsuccess = rslt_data['E_eV_atom'].count()\n",
    "nerror = ndata - nsuccess\n",
    "print('Success: {}'.format(nsuccess))\n",
    "print('Error: {}'.format(nerror))\n",
    "\n",
    "# ---------- minimum\n",
    "Emin = rslt_data['E_eV_atom'].min()\n",
    "print('Emin: {} eV/atom'.format(Emin))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Energy vs. trial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Energy (eV/atom)')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "# ---------- axis\n",
    "dx = 1\n",
    "ax.set_xlim([0, ndata+dx])\n",
    "ax.set_ylim([-0.5, 3])\n",
    "\n",
    "# ---------- hline at zero\n",
    "ax.hlines(0.0, -dx, ndata+dx, 'k', '--')\n",
    "\n",
    "# ---------- plot\n",
    "# x <-- ID + 1\n",
    "ax.plot(rslt_data.index + 1, rslt_data['E_eV_atom'] - Emin, 'o', ms=15, mew=2.0, alpha=0.8)\n",
    "\n",
    "# ---------- title and label\n",
    "ax.set_title('Random search for Si$_{16}$')\n",
    "ax.set_xlabel('Number of trials')\n",
    "ax.set_ylabel('Energy (eV/atom)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ---------- save figure\n",
    "fig.savefig('Si16_RS.png', bbox_inches='tight')    # PNG\n",
    "#fig.savefig('title_RS.png', bbox_inches='tight', dpi=300)    # high dpi PNG\n",
    "#fig.savefig('title_RS.pdf', bbox_inches='tight')    # PDF"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  },
  "toc": {
   "nav_menu": {
    "height": "84px",
    "width": "252px"
   },
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": "block",
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
